Abstract
In this work we combine the kernel method and the generalized half-quadratic criterion, and a kernel adaptive filtering algorithm is proposed based on the generalized half-quadratic criterion (KLGHQC). The generalized half-quadratic criterion (GHQC) guarantees the stability of the algorithm under the environment of the stable distribution noise, and the shape of the GHQC performance surface is determined by a constant, which improves the rate of convergence of the algorithm. Finally, the simulated results in two environments, Mackey–Glass sequence prediction and non-linear system identification. The outcome demonstrates that the KLGHQC algorithm proposed in this research outperforms other kernel filtering algorithms in the filtering accuracy and error magnitude.
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All data generated or analyzed during this study are included in this article. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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Yuanlian Huo performed the verification of the experimental designand graphing; Zikang Luo performed the first draft writing and investigation; and Liu Jie do the technique analysis.
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Huo, Y., Luo, Z. & Liu, J. A novel kernel filtering algorithm based on the generalized half-quadratic criterion. SIViP (2024). https://doi.org/10.1007/s11760-024-03394-9
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DOI: https://doi.org/10.1007/s11760-024-03394-9